Cancer Analysis
Services Guide
FOR RESEARCH USE ONLY
ILLUMINA PROPRIETARY
Document # 15040893 v01
December 2015
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Cancer Analysis Services Guide
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Document # 15040893 v01
Revision History
Date
Document
Document # 15040893
v01
December
2015
Part # 15040893 Rev. C
June 2015
Part # 15040893 Rev. B
November
2014
Part # 15040893 Rev. A
July 2013
Cancer Analysis Services Guide
Description of Change
• Revised documentation to reflect changes in version 6 of the
Illumina FastTrack Cancer Analysis Service pipeline.
• Renamed Isaac Structural Variant Caller, Isaac Somatic Variant
Caller, and Isaac Variant Caller to Manta, Strelka, and Starling,
respectively.
• Revised documentation to reflect changes in version 4 of the
Illumina FastTrack Cancer Analysis Service pipeline.
• Renamed Strelka and Manta to Isaac Somatic Variant Caller and
Isaac Structural Variant Caller, respectively.
Revised documentation to reflect changes in version 3 of the
Illumina FastTrack WGS pipeline.
Initial release.
v
Table of Contents
Revision History
Table of Contents
Chapter 1 Getting Started
v
vi
1
Introduction
Data Delivery
2
3
Chapter 2 Analysis Deliverables
4
Overview
Result Folder Structure
Somatic Variations Folder
Summary Report
Data Integrity
Chapter 3 Analysis Overview
Overview
Strelka (Somatic Small Variant Caller)
Manta (Large Indel and Structural Variant Caller)
Canvas (Copy Number Variations Caller)
Appendix A Appendix
Illumina FastTrack Services Annotation Pipeline
Technical Assistance
Cancer Analysis Services Guide
5
6
7
13
14
15
16
17
19
21
22
23
24
vi
Chapter 1 Getting Started
Introduction
Data Delivery
Cancer Analysis Services Guide
2
3
1
Chapter 1
Getting Started
Getting Started
Introduction
The Cancer Analysis Service Informatics Pipeline leverages a suite of proven algorithms
that are optimized for the complexities of tumor samples to deliver a set of accurate
somatic variants. Sequence reads are aligned and run through the Whole Genome
Sequencing workflow. The BAM files are then used as inputs into the Cancer Analysis
Service pipeline.
Software Packages
The Cancer Analysis Service pipeline uses the following software packages. For the
software versions used, see the Software Versions table in the summary PDF report
included with each deliverable.
Software
Strelka
Description
Joint tumor/normal small-variant caller.
Manta
Germline and Somatic SV (structural variant) caller. Calls SVs
between 50 bp and 10 kb. Candidate variants < 50 bp are passed
to Canvas.
Somatic CNV (copy number variant) caller > 10 kb. CNV calls
under 10 kb are produced, but set to a filtered status.
Canvas
Illumina
Annotation
Engine
2
Internal Annotation pipeline. Modeled from Ensembl's Variant
Effect Predictor (VEP).
Links
• Reference
• Availability
• Reference
• Availability
• Reference:
In Prep
• Availability
• Availability:
Internal
Only
Document # 15040893 v01
Illumina FastTrack Services currently provides data delivery through the following choices.
Illumina Hard Drive Data Delivery
Illumina FTS ships data on 1 or more hard drives. The hard drives are formatted with the
NTFS file system and can optionally be encrypted.
The data on the hard drive are organized in a folder structure with 1 top-level folder per
sample or analysis.
Illumina Cloud Data Delivery
Illumina FTS uploads data to a cloud container. Illumina currently supports uploads to the
Amazon S3 service. Upload data are organized per upload batch by date with an Illumina_
FTS prefix. For example, a sample in a batch uploaded on February 1, 2015 would be found
in the container with the prefix Illumina_FTS/20140201/SAMPLE_BARCODE. Contact your
FastTrack Services project manager to enable cloud delivery.
Cancer Analysis Services Guide
3
Data Delivery
Data Delivery
Chapter 2 Analysis Deliverables
Overview
Result Folder Structure
Somatic Variations Folder
Summary Report
Data Integrity
Cancer Analysis Services Guide
5
6
7
13
14
4
Chapter 2
Analysis Deliverables
Analysis Deliverables
Overview
This section details the files and folder structure for the cancer-normal somatic analysis
deliverable. Normal and paired tumor samples are batched together at delivery, but each
folder follows the same underlying format.
Though results from our Cancer Analysis Service are reported for tumor samples, the
algorithms have been designed for and tested on diploid samples, and not heterogenetic
tumor samples.
The files and folders generated for the cancer-normal somatic analysis results are based on
the unique sample identifiers for both the cancer and normal sample. Usually, these unique
identifiers are the barcodes associated with the cancer and normal samples in the lab, but
can be a known sample ID for reference samples.
5
Document # 15040893 v01
Under each paired tumor-normal sample folder, you can find the following file structure
that contains analysis results. Due to the quantity of DNA, samples run using our Nano
service do not have genotyping information.
For detailed information on assembly, genotyping, variations files, and descriptions of the
algorithms used to generate them, see the Whole Genome Sequencing Services User Guide,
document # 15040892.
Cancer[CancerSampleBarcode]_Normal[NormalSampleBarcode]
Metrics
Cancer[CancerSampleBarcode]_Normal[NormalSampleBarcode].Metrics.json
—JSON formatted statistics that mirror statistics from the summary PDF report
SomaticVariations
Cancer[CancerSampleBarcode]_Normal
[NormalSampleBarcode].somatic.vcf.gz—Single nucleotide variant and small
Insertion/Deletion somatic calls (1bp – 50bp) in *.vcf format
Cancer[CancerSampleBarcode]_Normal
[NormalSampleBarcode].somatic.SV.vcf.gz—Somatic Structural Variation somatic
calls (51bp + to 10kb) and somatic calls for regions with copy number aberrations
(CNAs) (10kb +) and loss of heterozygosity (LOH) in *.vcf format
md5sum.txt—Checksum file for confirming file consistency
Cancer[CancerSampleBarcode]_Normal
[NormalSampleBarcode].SummaryReport.pdf—PDF report containing a brief overview
of the somatic analysis results for the samples
NOTE
All the VCF files that Illumina provides are compressed and indexed using tabix. For details
about tabix, see the tabix manual in SAMtools (at samtools.sourceforge.net/tabix.shtml).
The tabix index shows up as an additional Cancer[CancerSampleBarcode]_Normal
[NormalSampleBarcode].TYPE.vcf.gz.tbi file.TYPE.vcf.gz.tbi file. It can be used for fast
retrieval of targeted regions in the associated *.vcf.gz file
NOTE
For some VCF files, a binary format of the annotations and their indexes are contained in
corresponding *.vcf.ant and *.vcf.ant.idx files respectively. If the *.vcf.ant file is maintained in
the same directory as its VCF file, the annotation information can be visualized alongside the
variant call information when imported to VariantStudio.
Cancer Analysis Services Guide
6
Result Folder Structure
Result Folder Structure
Analysis Deliverables
Somatic Variations Folder
The somatic variations folder contains all the variant calls produced for the somatic
analysis. The variant files that Illumina provides conform to the variant call format, VCF
4.1, specifications. For more information on the details of the VCF format, see
www.1000genomes.org/wiki/Analysis/Variant%20Call%20Format/vcf-variant-call-formatversion-41.
Cancer[CancerSampleBarcode]_Normal
[NormalSampleBarcode].somatic.vcf.gz
The somatic small variants VCF file contains small variants ≤ 50 bp that are called using
Strelka in VCF 4.1 format and annotated with the Illumina Annotation Engine.
This file contains the following metadata in the FILTER, FORMAT, and INFO fields.
FILTER Fields
ID
Description
BCNoiseIndel
Average fraction of filtered basecalls within 50 bases of the indel >
0.3.
HighDepth
Locus depth is > 3× mean chromosome depth in the normal sample.
LowQscore
The empirically fitted VQSR score is < 2.35.
QSI_ref
Normal sample is not homozygous ref or indel Q-score < 30 (ie, calls
with NT! = ref or QSI_NT < 30).
FORMAT Fields
7
ID
Description
AU
Number of A alleles used in tiers 1 and 2.
CU
Number of C alleles used in tiers 1 and 2.
DP
Read depth for tier 1 (used + filtered).
DP2
Read depth for tier 2.
DP50
Average tier 1 read depth within 50 bases.
FDP
Number of base calls filtered from original read depth for tier 1.
FDP50
Average tier 1 number of base calls filtered from original read depth
within 50 bases.
GU
Number of G alleles used in tiers 1 and 2.
SDP
Number of reads with deletions spanning this site in tier 1.
SUBDP
Number of reads below tier 1 mapping quality threshold aligned
across this site.
Document # 15040893 v01
Description
SUBDP50
Average number of reads below tier 1 mapping quality threshold
aligned across sites within 50 bases.
TAR
Reads strongly supporting alternate allele for tiers 1 and 2.
TIR
Reads strongly supporting indel allele for tiers 1 and 2.
TOR
Other reads (weak support or insufficient indel breakpoint overlap)
for tiers 1 and 2.
TU
Number of T alleles used in tiers 1 and 2.
Somatic Variations Folder
ID
INFO Fields
ID
Description
ALTMAP
Tumor alternate allele read position MAP.
ALTPOS
Tumor alternate allele read position median.
DP
Combined depth across samples.
IC
Number of times RU repeats in the indel allele.
IHP
Largest reference interrupted homopolymer length intersecting
with the indel.
MQ
IMS Mapping Quality.
MQ0
Number of MAPQ == 0 reads covering this record.
NT
Genotype of the normal sample in all data tiers, as used to classify
somatic variants. One of the following {ref,het,hom,conflict].
OVERLAP
Somatic indel possibly overlaps a second indel.
PNOISE
Fraction of panel containing nonreference noise at this site.
PNOISE2
Fraction of panel containing more than 1 nonreference noise obs at
this site.
QSI
Quality score for any somatic variant (ie, for the ALT haplotype to
be present at a significantly different frequency in the tumor and
normal sample).
QSI_NT
Quality score reflecting the joint probability of a somatic variant and
NT.
QSS
Quality score for any somatic SNV (ie, for the ALT allele to be
present a significantly different frequency in the tumor and normal
sample).
QSS_NT
Quality score reflecting the joint probability of a somatic variant and
NT.
RC
Number of times RU repeats in the reference allele.
Cancer Analysis Services Guide
8
Analysis Deliverables
ID
Description
ReadPosRankSum
Z=score from Wilcoxon rank sum test of Alt Vs. Ref read-position in
the tumor.
RU
Smallest repeating sequence unit in inserted or deleted sequence.
SGT
Most likely somatic genotype excluding normal noise states.
SNVSB
Somatic SNV site strand bias.
SOMATIC
Somatic mutation.
SVTYPE
Type of structural variant.
TQSI
Data tier used to compute QSI.
TQSI_NT
Data tier used to compute QSI_NT.
TQSS
Data tier used to compute QSS.
TQSS_NT
Data tier used to compute QSS_NT.
VQSR
Recalibrated quality score expressing the phred-scaled probability of
the somatic call being a FP observation.
Cancer[CancerSampleBarcode]_Normal
[NormalSampleBarcode].somatic.SV.vcf.gz
The somatic structural variants (SV) VCF file contains large variants > 50 bp that are called
using Manta and Canvas in VCF 4.1 format and annotated with the Illumina Annotation
Engine.
This file contains the following metadata in the ALT, FILTER, FORMAT, and INFO fields.
ALT Fields
ID
Description
BND
Translocation break-end.
CNV
Copy number variable region.
DEL
Deletion.
DUP:TANDEM
Tandem Duplication.
INS
Insertion.
INV
Inversion.
FILTER Fields
9
ID
Description
CLT10kb
Canvas call with length < 10 kb.
Document # 15040893 v01
Description
MaxDepth
Normal sample site depth is > 3× of the mean chromosome depth
near 1 or both variant break-ends.
MaxMQ0Frac
For a small variant (< 1000 bases) in the normal sample, the fraction
of reads with MAPQ0 around either break-end is > 0.4.
MinSomaticScore
Somatic score is < 30.
MGE10kb
Manta DEL or DUP call with length ≥ 10 kb.
q10
Quality < 10.
FORMAT Fields
ID
Description
BC
Number of bins in the region.
CN
Copy number genotype for imprecise events.
MCC
Major chromosome count (equal to copy number for LOH
regions).
PR
Spanning paired-read support for the ref and alt alleles in the order
listed.
RC
Mean counts per bin in the region.
SR
Split reads for the ref and alt alleles in the order listed, for reads
where P(allele|read) > 0.999.
INFO Fields
ID
Description
AA
The inferred allele ancestral (if determined) to the chimpanzee or
human lineage.
AF1000G
The allele frequency from all populations of 1000 genomes data.
BND_DEPTH
Read depth at local translocation break-end.
CIEND
Confidence interval around END.
CIGAR
CIGAR alignment for each alternate indel allele.
CIPOS
Confidence interval around POS.
clinvar
Clinical significance.
Format: GenotypeIndex|Significance
ColocalizedCanvas
Overlapped with a 10 kb+ Canvas call.
Cancer Analysis Services Guide
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Somatic Variations Folder
ID
Analysis Deliverables
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ID
Description
cosmic
The numeric identifier for the variant in the Catalogue of Somatic
Mutations in Cancer (COSMIC) database.
Format: GenotypeIndex|Significance
QSQR
Predicted regulatory consequence type.
Format: GenotypeIndex|RegulatoryID|Consequence
CSQT
Consequence type as predicted by IAE.
Format: GenotypeIndex|HGNC|Transcript ID|Consequence
END
End position of the variant described in this record.
EVENT
ID of event associated to break-end.
EVS
Allele frequency, coverage, and sample count taken from the
Exome Variant Server (EVS).
Format: AlleleFreqEVS|EVSCoverage|EVSSamples
GMAF
Global minor allele frequency (GMAF). Technically, the frequency
of the second most frequent allele.
Format: GlobalMinorAllele|AlleleFreqGlobalMinor
HOMLEN
Length of base pair identical microhomology at event breakpoints.
HOMSEQ
Sequence of base pair identical microhomology at event
breakpoints.
IMPRECISE
Imprecise structural variation.
INV3
Inversion break-ends open 3' of reported location.
INV5
Inversion break-ends open 5' of reported location.
JUNCTION_
SOMATICSCORE
If the SV junction is part of an EVENT (ie, a multiadjacency variant),
this field provides the SOMATICSCORE value only for the
adjacency in question.
LEFT_SVINSSEQ
Known left side of insertion of an insertion of unknown length.
MATE_BND_DEPTH
Read depth at remote translocation mate break-end.
MATEID
ID of mate break-end.
phyloP
PhyloP conservation score. Denotes how conserved the reference
sequence is between species throughout evolution.
RefMinor
Denotes positions where the reference base is a minor allele and is
annotated as though it were a variant.
RIGHT_SVINSSEQ
Known right side of insertion of an insertion of unknown length.
SOMATIC
Somatic mutation.
SOMATICSCORE
Somatic variant quality score.
SVINSLEN
Length of insertion.
SVINSSEQ
Sequence of insertion.
Document # 15040893 v01
Description
SVLEN
Difference in length between REF and ALT alleles.
SVTYPE
Type of structural variant.
Cancer Analysis Services Guide
Somatic Variations Folder
ID
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Analysis Deliverables
Summary Report
This PDF report contains a brief overview of the somatic analysis results for the samples
and contains the following sections.
Section
Description
Tumor
Sample
Information
Contains information associated with reads and alignment quality from the
tumor sequence input to the workflow.
Normal
Sample
Information
Contains information associated with reads and alignment quality from the
normal sequence input to the workflow.
Somatic
Small Variant
s Summary
Contains counts for the various types of reported small variants in the
small variants VCF file, split up by totals, sequence context, and
consequence (as calculated by the Illumina Annotation Engine).
Somatic
Structural
Variants
Summary
Contains counts for the various types of reported large variants in the
structural variants VCF file and counts of variants located in genes (as
calculated by the Illumina Annotation Engine).
Structural
Variants,
Tumor
Sample
Coverage,
and Allele
Frequency
Details the estimated purity and ploidy for the cancer sample output
from Canvas. For more information, see the Canvas Software Design
Document.
This section also includes a graph showing structural inversions, windowed
copy number aberration and loss of heterozygosity plots, and B-allele
frequency plots from Canvas.
Figure 1 Example of Structural Variants, Tumor Sample Coverage, and
Allele Frequency Graph
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Document # 15040893 v01
The md5sum.txt file is provided to check the integrity of the sample files and folders.
Immediately after sample quality check, the md5sums, or compact digital fingerprint, for
every file in the directory tree are generated. If media failures compromise data integrity,
you can use the md5sum tool to find the inconsistencies. Use the tool to compare the hash
from the provided md5sum file to the hash generated from the downloaded file.
On a Unix system, you can use the following commands to perform an md5sum check,
assuming the utility is installed:
% cd [Sample_Barcode]
% md5sum –c md5sum.txt
The check verifies every file in ~30–45 minutes. Any errors are listed in the output.
In Windows, there are various command line and GUI tools available to perform an
md5sum check. The Cygwin tools provide a utility identical to Linux.
Cancer Analysis Services Guide
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Data Integrity
Data Integrity
Chapter 3 Analysis Overview
Overview
Strelka (Somatic Small Variant Caller)
Manta (Large Indel and Structural Variant Caller)
Canvas (Copy Number Variations Caller)
Cancer Analysis Services Guide
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Chapter 3
Analysis Overview
Analysis Overview
Overview
The somatic variant calling pipeline uses a normal BAM file and a tumor BAM file as
input. In the tumor analysis pipeline, these BAM files are the result of the whole-genome
sequencing pipeline described in the Whole-Genome Sequencing Services User Guide (document
# 15040892).
These BAM files are then processed through 3 interconnected callers:
} Somatic Small Variant Calls (Strelka)
} Large Indel and Structural Variant Caller (Manta)
} Copy Number Variations Caller (Canvas)
During the first stage of the pipeline, the tumor and normal BAM files run through a
combined indel realignment operation. This realignment operation is used as the input for
further processing. During calling, putative calls and de novo reassembled sections of
sequence are passed between the callers to produce internally consistent variant calls.
All 3 callers use statistical models that operate on the combined tumor and normal reads
as input instead of the variants. The statistical models use combined calling instead of
subtraction of variant calls. Using combined calling produces superior results. However,
subtraction of the calls from the normal and tumor whole genome results often do not
match the somatic calls from a combined caller. For example, you can find a somatic
variant that was not called in the tumor WGS sample because the combined caller is
operating on the reads.
Figure 2 Cancer Analysis Pipeline
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Document # 15040893 v01
The somatic small variant calling method (Strelka) detects somatic SNVs and indels in
sequencing data from a tumor and matched normal sample. For more information, see the
publication Strelka: accurate somatic small-variant calling from sequenced tumor-normal
sample pairs or post to the Strelka mailing list.
The analysis is based on the following assumptions:
} The normal sample is a mixture of diploid germline variation and noise.
} The tumor sample is a combination of the normal sample and somatic variation. It is
assumed that the somatic variation and the normal noise can occur at any allele
frequency ratio.
For SNVs, but not for indels, the normal noise component is further modeled as a
combination of single-strand and double-strand noise.
Figure 3 Strelka Method
NOTE
For a detailed overview of Strelka methods, go to www.ncbi.nlm.nih.gov/pubmed/22581179.
Saunders,C.T., Wong,W.S., Swamy,S. et al. (2012) Strelka: accurate somatic small-variant calling
from sequenced tumor-normal sample pairs. Bioinformatics, 28, 1811–1817. doi:
10.1093/bioinformatics/bts271
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Strelka (Somatic Small Variant Caller)
Strelka (Somatic Small Variant Caller)
Analysis Overview
Candidate Indel Search
Strelka scans through the genome using sequence alignments from the normal sample and
tumor sample together to find a joint set of candidate indels. The information in sequence
alignments is supplemented with externally generated candidate indels discovered by
Manta. Manta provides external candidate indels to Strelka for indels of size 50 and below.
Candidate indels are used for realignment of reads, during which each candidate indel is
evaluated as a potential somatic indel. Any other types of indels are considered noise
indels. If a better alignment is not found, these indels are allowed to remain in the read
alignments; otherwise, they are not used.
The candidate indel thresholds are designed so that the joint candidate indel set is at least
the combined set found if the Small Variant Caller (Starling) is run on the individual
samples. Specifically, where a minimum number of nominating reads is required for
candidacy in Starling, Strelka requires the same minimum number of nominating reads
from the combined input. Strelka requires that at least 1 sample contains a minimum
fraction of supporting reads among the sample reads for candidacy.
For more information on Starling, see the Whole-Genome Sequencing Services User Guide,
document # 15040892.
Realignment
For every read that intersects a candidate alignment, the Strelka attempts to find the most
probable alignments including the candidate indel and excluding the candidate indel.
Typically, the alignment excluding the candidate indel aligns to the reference, but
occasionally an alternate indel that overlaps or interferes with the candidate is found to be
more likely. The indel caller uses the probabilities of both alignments as part of the indel
quality score calculation, whereas only a single alignment (usually the most probable) is
preserved for SNV calling.
Somatic Caller
Strelka uses a Bayesian probability model similar to the one used for germline variant
calling in the Starling Small Variant Caller or in external tools such as GATK. Using this
model, our objective is to compute the posterior probability P(θ│ D), which is the
probability of the model state θ conditioned on the observed sequencing data.
In a germline variant caller, the state space of the model is conventionally a discrete set of
diploid genotypes. For SNVs, the set of possible states is G=
{"AA,CC,GG,TT,AC,AG,AT,CG,CT,GT"}.
The Strelka model instead approximates continuous allele frequencies for each allele:
f={f_A, f_C, f_G, f_T}
The allele frequencies are restricted to allow a maximum of 2 nonzero frequencies. Any
additional alleles observed in the data are treated as noise.
Another departure from typical germline calling methods is that the state space of the
model is the allele frequency of both the tumor and the normal sample. In the following
equation, f_t and f_n represent the allele frequencies of the tumor and normal samples,
respectively.
θ=(f_t, f_n)
The final somatic variant quality value reported by the model is computed from the
probability that the allele frequencies are unequal (ie, f_t≠f_n) given the observed sequence
data.
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Document # 15040893 v01
The large indel and structural variant calling method (Manta) is a structural variant caller
for short sequencing reads. It can discover structural variants of any size and score these
variants using both a diploid genotype model and a somatic model (when separate tumor
and normal samples are specified). Structural variant discovery and scoring incorporate
both paired read fragment spanning and split read evidence.
For more information, see the publication Manta: Rapid detection of structural variants and
indels for clinical sequencing applications or the Manta GitHub.
Chen,X., Schulz-Trieglaff,O., Shaw,R. et al. (2015) Manta: Rapid detection of structural variants
and indels for clinical sequencing applications. Bioinformatics. Advance online publication. doi:
10.1101/024232
Method Overview
Manta works by dividing the structural variant discovery process into 2 primary steps–
scanning the genome to find SV associated regions and analysis, scoring, and output of
SVs found in such regions.
1
Build SV association graph
Scan the entire genome to discover evidence of possible SVs and large indels. This
evidence is enumerated into a graph with edges connecting all regions of the genome
that have a possible SV association. Edges can connect 2 different regions of the
genome to represent evidence of a long-range association, or an edge can connect a
region to itself to capture a local indel/small SV association. These associations are
more general than a specific SV hypothesis, in that many SV candidates can be found
on 1 edge, although typically only 1 or 2 candidates are found per edge.
2
Analyze graph edges to find SVs
Analyze individual graph edges or groups of highly connected edges to discover and
score SVs associated with the edges. The substeps of this process include:
} Inference of SV candidates associated with the edge.
} Attempted assembly of the SVs break-ends.
} Scoring and filtration of the SV under various biological models (currently diploid
germline and somatic).
} Output to VCF.
Capabilities
Manta can detect all structural variant types that are identifiable in the absence of copy
number analysis and large scale de novo assembly. Detectable types are enumerated in this
section.
For each structural variant and indel, Manta attempts to align the break-ends to base pair
resolution and report the left-shifted break-end coordinate (per the VCF 4.1 SV reporting
guidelines). Manta also reports any break-end microhomology sequence and inserted
sequence between the break-ends. Often the assembly fails to provide a confident
explanation of the data. In such cases, the variant is reported as IMPRECISE, and scored
according to the paired-end read evidence alone.
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Manta (Large Indel and Structural Variant
Manta (Large Indel and Structural Variant Caller)
Analysis Overview
The sequencing reads provided as input to Manta are expected to be from a paired-end
sequencing assay that results in an inwards orientation between the 2 reads of each DNA
fragment. Each read presents a read from the outer edge of the fragment insert inward.
Detected Variant Classes
Manta is able to detect all variation classes that can be explained as novel DNA
adjacencies in the genome. Simple insertion/deletion events can be detected down to a
configurable minimum size cutoff (defaulting to 51). All DNA adjacencies are classified
into the following categories based on the break-end pattern:
} Deletions
} Insertions
} Inversions
} Tandem Duplications
} Interchromosomal Translocations
Known Limitations
Manta cannot detect the following variant types:
} Nontandem repeats/amplifications
} Large insertions—The maximum detectable size corresponds to approximately the
read-pair fragment size, but note that detection power falls off to impractical levels well
before this size.
} Small inversions—The limiting size is not tested, but in theory detection falls off below
~200 bases. So-called microinversions might be detected indirectly as combined
insertion/deletion variants.
More general repeat-based limitations exist for all variant types:
} Power to assemble variants to break-end resolution falls to 0 as break-end repeat length
approaches the read size.
} Power to detect any break-end falls to (nearly) 0 as the break-end repeat length
approaches the fragment size.
} The method cannot detect nontandem repeats.
While Manta classifies novel DNA-adjacencies, it does not infer the higher level constructs
implied by the classification. For instance, a variant marked as a deletion by Manta
indicates an intrachromosomal translocation with a deletion-like break-end pattern.
However, there is no test of depth, b-allele frequency, or intersecting adjacencies to infer the
SV type directly.
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Document # 15040893 v01
Canvas is a tool for calling copy number variants (CNVs) from human DNA sequencing
data. Canvas can work with either germline data or paired tumor/normal samples. The
primary input is aligned reads in BAM format and the primary output is a report VCF file
that gives the copy number status of the genome.
For a description of Canvas and its algorithms, see the Canvas Software Design Document.
Cancer Analysis Services Guide
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Canvas (Copy Number Variations Caller)
Canvas (Copy Number Variations Caller)
Appendix A Appendix
Appendix
Illumina FastTrack Services Annotation Pipeline
Cancer Analysis Services Guide
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22
Appendix
Illumina FastTrack Services Annotation Pipeline
The FastTrack pipeline is an internal pipeline that provides the following annotations.
NOTE
These versions are specific to the time of publication of this document and can change with
later updates. To determine the versions used, see the VCF file headers.
Source
dbSNP
COSMIC
1000 Genomes Project
EVS
ClinVar
phyloP
In
}
}
}
23
Version
144
v73
Phase 3 v5a
V2
Unknown
hg19
Release Date
06/06/2015
06/06/2015
05/27/2013
11/13/2013
09/02/2015
11/10/2009
addition, the following annotations are added:
Consequence predictions on RefSeq and Ensembl transcripts (modeled from VEP)
Annotations in regulatory elements (modeled from VEP)
Gene/transcript identifiers and their relationship between RefSeq, Ensembl, HGNC, and
known synonyms (Gene Index)
Document # 15040893 v01
For technical assistance, contact Illumina Technical Support.
Table 1 Illumina General Contact Information
Website
Email
www.illumina.com
[email protected]
Table 2 Illumina Customer Support Telephone Numbers
Region
Contact Number
Region
North America
1.800.809.4566
Japan
Australia
1.800.775.688
Netherlands
Austria
0800.296575
New Zealand
Belgium
0800.81102
Norway
China
400.635.9898
Singapore
Denmark
80882346
Spain
Finland
0800.918363
Sweden
France
0800.911850
Switzerland
Germany
0800.180.8994
Taiwan
Hong Kong
800960230
United Kingdom
Ireland
1.800.812949
Other countries
Italy
800.874909
Contact Number
0800.111.5011
0800.0223859
0800.451.650
800.16836
1.800.579.2745
900.812168
020790181
0800.563118
00806651752
0800.917.0041
+44.1799.534000
Safety data sheets (SDSs)—Available on the Illumina website at
support.illumina.com/sds.html.
Product documentation—Available for download in PDF from the Illumina website. Go
to support.illumina.com, select a product, then select Documentation & Literature.
Cancer Analysis Services Guide
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Technical Assistance
Technical Assistance
Illumina
5200 Illumina Way
San Diego, California 92122 U.S.A.
+1.800.809.ILMN (4566)
+1.858.202.4566 (outside North America)
[email protected]
www.illumina.com
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